Efficient polyp segmentation in healthcare plays a critical role in enabling early diagnosis of colorectal cancer. However, the segmentation of polyps presents numerous challenges, including the intricate distribution of backgrounds, variations in polyp sizes and shapes, and indistinct boundaries. Defining the boundary between the foreground (i.e. polyp itself) and the background (surrounding tissue) is difficult. To mitigate these challenges, we propose Multi-Scale Edge-Guided Attention Network (MEGANet) tailored specifically for polyp segmentation within colonoscopy images. This network draws inspiration from the fusion of a classical edge detection technique with an attention mechanism. By combining these techniques, MEGANet effectively preserves high-frequency information, notably edges and boundaries, which tend to erode as neural networks deepen. MEGANet is designed as an end-to-end framework, encompassing three key modules: an encoder, which is responsible for capturing and abstracting the features from the input image, a decoder, which focuses on salient features, and the Edge-Guided Attention module (EGA) that employs the Laplacian Operator to accentuate polyp boundaries. Extensive experiments, both qualitative and quantitative, on five benchmark datasets, demonstrate that our EGANet outperforms other existing SOTA methods under six evaluation metrics. Our code is available at \url{https://github.com/UARK-AICV/MEGANet}.

翻译：在医疗保健中，高效的息肉分割对于结直肠癌的早期诊断至关重要。然而，息肉分割面临诸多挑战，包括背景分布复杂、息肉大小和形状多变以及边界模糊。定义前景（即息肉本身）与背景（周围组织）之间的边界十分困难。为应对这些挑战，我们提出了一种专门用于结肠镜图像中息肉分割的多尺度边缘引导注意力网络（MEGANet）。该网络受经典边缘检测技术与注意力机制融合的启发。通过结合这些技术，MEGANet有效保留了高频信息，尤其是边缘和边界——这些信息会随神经网络深度增加而逐渐丢失。MEGANet设计为端到端框架，包含三个关键模块：编码器（负责从输入图像中捕获和抽象特征）、解码器（聚焦于显著特征）以及边缘引导注意力模块（EGA），该模块利用拉普拉斯算子强化息肉边界。在五个基准数据集上进行的定性和定量实验表明，我们的MEGANet在六项评估指标下优于现有其他最先进方法。我们的代码已开源在\url{https://github.com/UARK-AICV/MEGANet}。